Dolma

Dolma is a dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials.

More information:

• Read Dolma manuscript and its Data Sheet on ArXiv;
• Explore the open source tools we created to curate Dolma.
• Want to request removal of personal data? Use this form to notify us of documents containing PII about a specific user.

To learn more about the toolkit used to create Dolma, including how to replicate this dataset, head over our GitHub project page!

2024-04-17: Dolma v1.7 Release. We have released an updated version of Dolma that we used to train our latest OLMo 7B-v1.7 model.

2024-04-15: License Change. We have updated the license of Dolma to ODC-BY. Please see this blog post for more information.

Versions

At the moment, there are six versions of Dolma available:

Version	Default?	Release Date	Size (gzip)	Description
v1_7	✅	2024-04-15	4.5 TB	Used to train OLMo-7B-v1.7. New sources, more quality filtering, fuzzy deduplication.
v1_6		2024-01-31	5.4 TB	An update to v1.5 with some deduplication of documents with too few tokens or too many repeated n-grams.
v1_6-sample		2024-01-31	16.4 GB	A smaller sample of Dolma, with roughly 10 billion tokens. Useful for data exploration.
v1_5		2023-10-31	6.4 TB	Used to train OLMo-1B. Roughly 3 trillion tokens.
v1_5-sample		2023-10-31	2.9 TB	A sample of roughly 1.9 trillion tokens used to train OLMo-7B
v1		2023-08-18	6.0 TB	The first version of Dolma.

Summary Statistics (v1.7)

Source	Provenance	New?	Documents (millions)	OLMo tokens (billions)	Sample Proportion	Cutoff Date	Processing
Dolma's CC	Common Crawl via Dolma v1.6	Updated	875.2	1,195.5	50%	Mar 2023	Extracted using the Dolma pipeline; new quality filtering and deduplication steps.
Refined Web	Refined Web	Yes	664.0	456.4	100%	Feb 2023	Filtered using the Dolma pipeline; new quality filtering and deduplication steps.
StarCoder	StarCoder	Yes	206.6	263.8	100%	May 2023	No further processing.
C4	C4 via Dolma v1.6	Updated	249.9	138.4	50%	Apr 2019	Filtered using the Dolma pipeline; new quality filtering and deduplication steps.
Reddit	PushShift API	Updated	377.4	79.9	100%	Mar 2023	Extracted using the Dolma pipeline; new quality filtering and deduplication steps.
Semantic Scholar (S2ORC & S2AG)	peS2o via Dolma v1.6	No	38.8	57.2	100%	Mar 2023	Same as Dolma v1.6
arXiv	RedPajama v1	Yes	1.5	28.0	100%	Mar 2023	No further processing.
StackExchange	RedPajama v1	Yes	29.3	19.6	100%	Mar 2023	No further processing.
Flan	Flan Collection, reproduced following the original code, as performed by Dettmers et al., (2023)	Yes	52.1	16.5	100%	Feb 2023	After reproducing Flan, sampled to balance different Flan subsets. Reformatted for pretraining with newlines separating instruction and demonstration.
CC News	Common Crawl	Yes	22.0	14.3	100%	Mar 2023	Extracted using the Dolma pipeline; new quality filtering and deduplication steps.
OpenWebMath	OpenWebMath via Proof Pile II	Yes	2.9	12.6	100%	May 2023	Training subset; no further processing.
Algebraic Stack	Proof Pile II	Yes	2.8	12.6	100%	Oct 2023	Training subset; no further processing.
Project Gutenberg	Project Gutenberg via Dolma v1.6	No	0.0556	5.3	100%	Mar 2023	Same as Dolma v1.6
MegaWika	MetaWika	Yes	3.2	4.6	100%	Jul 2023	English web pages cited from Wikipedia; curated using the full Dolma pipeline.
Wikipedia & Wikibooks	Wikimedia via Dolma v1.6	No	6.2	3.7	200%	Mar 2023	Same as Dolma v1.6
Total			2532.0	2,308.5	1,715.1	Oct 2023

(A subset of total data was used for training of OLMo 7B-v1.7. The token counts are based on the full dataset, whereas taking into account sampling proportion gives the final actual token counts used for training --- 1.715 trillion tokens.)

Summary Statistics (v1.6)

Source	Doc Type	UTF-8 bytes (GB)	Documents (millions)	Unicode words (billions)	Llama tokens (billions)
Common Crawl	web pages	9,022	3,370	1,775	2,281
The Stack	code	1,043	210	260	411
C4	web pages	790	364	153	198
Reddit	social media	339	377	72	89
PeS2o	STEM papers	268	38.8	50	70
Project Gutenberg	books	20.4	0.056	4.0	6.0
Wikipedia, Wikibooks	encyclopedic	16.2	6.2	3.7	4.3
Total		11,519	4,367	2,318	3,059

Download

The fastest way to download Dolma is to clone this repository and use the files in the url directory. We recommend using wget in parallel mode to download the files. For example:

DATA_DIR="<path_to_your_data_directory>"
PARALLEL_DOWNLOADS="<number_of_parallel_downloads>"
DOLMA_VERSION="<version_of_dolma_to_download>"

git clone https://huggingface.co/datasets/allenai/dolma
mkdir -p "${DATA_DIR}"


cat "dolma/urls/${DOLMA_VERSION}.txt" | xargs -n 1 -P "${PARALLEL_DOWNLOADS}" wget -q -P "$DATA_DIR"

Then, to load this data using HuggingFace's datasets library, you can use the following code:

import os
from datasets import load_dataset

os.environ["DATA_DIR"] = "<path_to_your_data_directory>"
dataset = load_dataset("allenai/dolma", split="train")

Licensing Information

We are releasing this dataset under the terms of ODC-BY. By using this dataset, you are also bound any license agreements and terms of use of the original data sources.

Bibtex

If you use our dataset or tooling, please cite us at:

@article{dolma,
  title = {{Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}},
  author={
    Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and
    Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and
    Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and
    Nathan Lambert and Ian Magnusson and Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and
    Crystal Nam and Matthew E. Peters and Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and
    Emma Strubell and Nishant Subramani and Oyvind Tafjord and Pete Walsh and Luke Zettlemoyer and
    Noah A. Smith and Hannaneh Hajishirzi and Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo
  },
  year = {2024},
  journal={arXiv preprint},
}